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Nuclio Learning

Expert in AI and Data Science Theory and Practice

  • up to 43 hours
  • Beginner

Dive into the world of AI and Data Science step by step, from theory and AI ethics with an executive focus to action and practice in your Data Science projects. This course offers a dual certification from Nuclio Digital School and the University EUNEIZ, providing a comprehensive understanding of AI and Data Science fundamentals.

  • AI Fundamentals
  • Data Science
  • Python Programming
  • Data Analysis with Pandas
  • Machine Learning

Overview

This course will guide you through the essentials of AI and Data Science, starting from data-driven business strategies to technological platforms. You'll gain advanced knowledge in programming, data analysis with Pandas, and creating impactful dashboards with PowerBI. The course also covers AI model development, enabling you to make real-world data predictions.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    Spanish
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Professionals from various fields

Individuals seeking to understand the fundamentals of artificial intelligence in the business world.

Aspiring Data Analysts

Those looking to take their first steps in Data Science with Python and execute their own projects.

Business Intelligence Professionals

Professionals aiming to enhance their understanding of AI and Data Science for business applications.

Why should you take this course?

Data

This course offers a comprehensive understanding of AI and Data Science, covering key topics like Python programming, data analysis, and machine learning. It's ideal for beginners and professionals looking to advance their careers by gaining practical skills in AI and Data Science.

Pre-Requisites

1 / 3

  • Basic understanding of business concepts

  • Interest in data science and AI

  • Willingness to learn Python

What will you learn?

Module 1 - Introduction
Welcome and Introduction to the Course. Overview of what to expect, course objectives, and a brief description of the course structure.
Module 2 - Data-Driven Business Strategy
Evolution of business strategy from marketing to product, Blue Ocean strategies, customer-centric approach, and data challenges.
Module 3 - Data Platforms and Technologies: Relational Databases
Evolution of Data Technologies and AI, data generation systems, relational databases, SQL, and business intelligence.
Module 4 - Data Platforms and Technologies: Non-Relational Databases (NoSQL)
Structured, semi-structured, and unstructured data, NoSQL databases, data lake vs data warehouse, and Big Data.
Module 5 - Data Platforms and Technologies: The Cloud
Introduction to cloud computing, its characteristics, benefits, service models, and practical example with Google Cloud.
Module 6 - Programming Languages
Introduction to programming languages, types, most used languages, and current trends.
Module 7 - Data Science
Definition of analytics and data science, roles, scientific method, algorithms, machine learning, and AI.
Module 8 - Machine Learning
Types of machine learning problems, supervised, unsupervised, and reinforcement learning, and their main algorithms.
Module 9 - Deep Learning
Introduction to deep learning, neural networks, challenges, architectures, and specialized hardware.
Module 10 - Generative AI
Introduction to generative AI, applications, tools, ChatGPT, and business model of ChatGPT (OpenAI).
Module 11 - Data and AI Ethics
Ethics in AI, fundamental ethical principles, real cases, privacy, data protection, and AI ethics in media.
Module 12 - Conclusion
Course content summary, review of course objectives, and acknowledgments.
Module 13 - Python: Basic Fundamentals
Overview of programming languages, introduction to Python, and its presence in the current world.
Module 14 - Python: Control Flow and Basic Data Structures
Variables, operators, and conditionals in Python.
Module 15 - Python: Functions
Definition and calling of functions, parameters, and arguments.
Module 16 - Pandas Library: Basic Fundamentals
Introduction to Pandas and its importance in data analysis, series, and dataframes.
Module 17 - Pandas Library: Handling DataFrames and Series
Creation and basic manipulation of series and dataframes, descriptive analysis, filtering, and cleaning.
Module 18 - Visualization with PowerBI
Introduction to dashboards, PowerBI, and installation.
Module 19 - Machine Learning with Python
Introduction to machine learning, problem types, classification, regression, and practice.
Module 20 - EDA and First Contact with the Dataset
Exploratory Data Analysis (EDA) and initial dataset interaction.
Module 21 - sklearn and Best Practices for Data Processing
Introduction to sklearn, train-test split, transformers, pipelines, and object-oriented programming.
Module 22 - Code Refactor, Wrap Up, and Conclusions
Code refactoring, final wrap-up, and conclusions.

Meet your instructors

  • Felipe Calderero

    Head of Software Engineering | Gen AI Expert | AI Professor , MOSTLY AI

    Felipe Calderero is a hands-on CTO using AI and data to enhance quality of life and broaden technology access. Having transitioned from a rich academic background to the tech industry, he now focuses on turning research into actionable solutions.

  • Ignacio Anguita

    Quantitative Analyst/Developer, SAS

    Ignacio Anguita is an experienced Quant Developer and Portfolio Manager currently working at SAS. He has priced and hedged various financial instruments including exotic inflation deals and equity and fixed income derivatives. He has also managed large fixed income portfolios and developed tools for traders and systematic strategies in multiple programming languages.

  • Núria Xifré Martín

    Senior Data Engineer, Nuclio Learning

    Núria is a passionate data and software engineer with a background in Aerospace Engineering. She is currently working as a Senior Data Engineer at AstraZeneca.

  • Nico Popescul

    Data Science Manager, Caixabank Advanced Business Analytics

    Nico Popescul is a Data Science Manager at Caixabank Advanced Business Analytics. He has a background in data science and machine learning.

Upcoming cohorts

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€639